Machine learning techniques for short-term solar power stations operational mode planning

The paper presents the operational model of very-short term solar power stations (SPS) generation forecasting developed by the authors, based on weather information and built into the existing software product as a separate module for SPS operational forecasting. It was revealed that one of the optimal mathematical methods for SPS generation operational forecasting is gradient boosting on decision trees. The paper describes the basic principles of operational forecasting based on the boosting of decision trees, the main advantages and disadvantages of implementing this algorithm. Moreover, this paper presents an example of this algorithm implementation being analyzed using the example of data analysis and forecasting the generation of the existing SPS.

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Very-short term solar power generation forecasting based on trend-additive and seasonal-multiplicative smoothing methodology

E3S Web of Conferences ◽

10.1051/e3scconf/20185102003 ◽

2018 ◽

Vol 51 ◽

pp. 02003

Author(s):

Stanislav Eroshenko ◽

Alexandra Khalyasmaa ◽

Rustam Valiev

Keyword(s):

Solar Power ◽

Forecast Error ◽

Renewable Energy Sources ◽

Power Station ◽

Short Term ◽

Operating Modes ◽

Power Stations ◽

Operational Forecasting ◽

Solar Power Station ◽

System Operating

In conditions of development of generating facilities on renewable energy sources, the technology runs up to uncertainty in the operational and short-term planning of the power system operating modes. To date, reliable tools for forecasting the generation of solar power stations are required. This paper considers the methodology of operational forecasting of solar power stations output based on the mathematical apparatus of cubic exponential smoothing with trend and seasonal components. The presented methodology was tested based on the measuring data of a real solar power station. The average forecast error was not more than 10% for days with variable clouds and not more than 3% for clear days, which indicates the effectiveness of the proposed approach.

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Very-short term solar power generation forecasting based on trend-additive and seasonal-multiplicative smoothing methodology

E3S Web of Conferences ◽

10.1051/e3sconf/20185102003 ◽

2018 ◽

Vol 51 ◽

pp. 02003

Author(s):

Stanislav Eroshenko ◽

Alexandra Khalyasmaa ◽

Rustam Valiev

Keyword(s):

Solar Power ◽

Forecast Error ◽

Renewable Energy Sources ◽

Power Station ◽

Short Term ◽

Operating Modes ◽

Power Stations ◽

Operational Forecasting ◽

Solar Power Station ◽

System Operating

In conditions of development of generating facilities on renewable energy sources, the technology runs up to uncertainty in the operational and short-term planning of the power system operating modes. To date, reliable tools for forecasting the generation of solar power stations are required. This paper considers the methodology of operational forecasting of solar power stations output based on the mathematical apparatus of cubic exponential smoothing with trend and seasonal components. The presented methodology was tested based on the measuring data of a real solar power station. The average forecast error was not more than 10% for days with variable clouds and not more than 3% for clear days, which indicates the effectiveness of the proposed approach.

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Large-Scale Linear RankSVM

Neural Computation ◽

10.1162/neco_a_00571 ◽

2014 ◽

Vol 26 (4) ◽

pp. 781-817 ◽

Cited By ~ 48

Author(s):

Ching-Pei Lee ◽

Chih-Jen Lin

Keyword(s):

Decision Trees ◽

Computational Efficiency ◽

Efficient Algorithm ◽

Large Scale ◽

Learning To Rank ◽

Gradient Boosting ◽

Baseline Model ◽

Nonlinear Methods ◽

Advantages And Disadvantages ◽

Linear Ranksvm

Linear rankSVM is one of the widely used methods for learning to rank. Although its performance may be inferior to nonlinear methods such as kernel rankSVM and gradient boosting decision trees, linear rankSVM is useful to quickly produce a baseline model. Furthermore, following its recent development for classification, linear rankSVM may give competitive performance for large and sparse data. A great deal of works have studied linear rankSVM. The focus is on the computational efficiency when the number of preference pairs is large. In this letter, we systematically study existing works, discuss their advantages and disadvantages, and propose an efficient algorithm. We discuss different implementation issues and extensions with detailed experiments. Finally, we develop a robust linear rankSVM tool for public use.

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Weather data errors analysis in solar power stations generation forecasting

E3S Web of Conferences ◽

10.1051/e3sconf/20185102002 ◽

2018 ◽

Vol 51 ◽

pp. 02002 ◽

Cited By ~ 2

Author(s):

Stanislav Eroshenko ◽

Alexandra Khalyasmaa

Keyword(s):

Detailed Analysis ◽

Solar Power ◽

Weather Data ◽

Forecasting Model ◽

Short Term ◽

Data Errors ◽

Power Stations ◽

Software Product ◽

Short Term Forecasting ◽

Errors Analysis

The paper presents a short-term forecasting model for solar power stations (SPS) generation developed by the authors. This model is based on weather data and built into the existing software product as a separate short-term forecasting module for the SPS generation. The main problems associated with forecasting the SPS generation on cloudy days were revealed in the framework of authors' research, which is due not to the error of the developed model but to the use of the same learning sample for both solar and cloudy days. This paper contains analysis of the main problems related to the learning sampling, samples pattern, quality and representativeness for forecasting the SPS generation on cloudy days. Besides, the paper includes a calculation example performed for the existing SPS and a detailed analysis of the forecast generation on cloudy days based on the actual weather provider data.

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Weather data errors analysis in solar power stations generation forecasting

E3S Web of Conferences ◽

10.1051/e3scconf/20185102002 ◽

2018 ◽

Vol 51 ◽

pp. 02002 ◽

Cited By ~ 1

Author(s):

Stanislav Eroshenko ◽

Alexandra Khalyasmaa

Keyword(s):

Detailed Analysis ◽

Solar Power ◽

Weather Data ◽

Forecasting Model ◽

Short Term ◽

Data Errors ◽

Power Stations ◽

Software Product ◽

Short Term Forecasting ◽

Errors Analysis

The paper presents a short-term forecasting model for solar power stations (SPS) generation developed by the authors. This model is based on weather data and built into the existing software product as a separate short-term forecasting module for the SPS generation. The main problems associated with forecasting the SPS generation on cloudy days were revealed in the framework of authors' research, which is due not to the error of the developed model but to the use of the same learning sample for both solar and cloudy days. This paper contains analysis of the main problems related to the learning sampling, samples pattern, quality and representativeness for forecasting the SPS generation on cloudy days. Besides, the paper includes a calculation example performed for the existing SPS and a detailed analysis of the forecast generation on cloudy days based on the actual weather provider data.

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Predicting Short-Term Subway Ridership and Prioritizing Its Influential Factors Using Gradient Boosting Decision Trees

Sustainability ◽

10.3390/su8111100 ◽

2016 ◽

Vol 8 (11) ◽

pp. 1100 ◽

Cited By ~ 52

Author(s):

Chuan Ding ◽

Donggen Wang ◽

Xiaolei Ma ◽

Haiying Li

Keyword(s):

Decision Trees ◽

Influential Factors ◽

Gradient Boosting ◽

Short Term

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Prediction of Mean Wave Overtopping Discharge Using Gradient Boosting Decision Trees

Water ◽

10.3390/w12061703 ◽

2020 ◽

Vol 12 (6) ◽

pp. 1703 ◽

Cited By ~ 3

Author(s):

Joost P. den Bieman ◽

Josefine M. Wilms ◽

Henk F. P. van den Boogaard ◽

Marcel R. A. van Gent

Keyword(s):

Machine Learning ◽

Decision Trees ◽

Numerical Models ◽

Input Parameter ◽

Design Criterion ◽

Machine Learning Techniques ◽

Gradient Boosting ◽

Wave Overtopping ◽

Learning Techniques ◽

Machine Learning Model

Wave overtopping is an important design criterion for coastal structures such as dikes, breakwaters and promenades. Hence, the prediction of the expected wave overtopping discharge is an important research topic. Existing prediction tools consist of empirical overtopping formulae, machine learning techniques like neural networks, and numerical models. In this paper, an innovative machine learning method—gradient boosting decision trees—is applied to the prediction of mean wave overtopping discharges. This new machine learning model is trained using the CLASH wave overtopping database. Optimizations to its performance are realized by using feature engineering and hyperparameter tuning. The model is shown to outperform an existing neural network model by reducing the error on the prediction of the CLASH database by a factor of 2.8. The model predictions follow physically realistic trends for variations of important features, and behave regularly in regions of the input parameter space with little or no data coverage.

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A Gradient Boosting Model Optimized by a Genetic Algorithm for Short-term Riverflow Forecast

Revista Mundi Engenharia Tecnologia e Gestão (ISSN 2525-4782) ◽

10.21575/25254782rmetg2019vol4n3845 ◽

2019 ◽

Vol 4 (3) ◽

Author(s):

Tales Lima Fonseca ◽

Yulia Gorodetskaya ◽

Gisele Goulart Tavares ◽

Celso Bandeira de Melo Ribeiro ◽

Leonardo Goliatt da Fonseca

Keyword(s):

Machine Learning ◽

Genetic Algorithm ◽

Predictive Performance ◽

Energy Generation ◽

Machine Learning Techniques ◽

Gradient Boosting ◽

Short Term ◽

Learning Techniques ◽

Hydrometeorological Parameters ◽

Paraíba Do Sul River

The short-term streamflow forecast is an important parameter in studies related to energy generation and the prediction of possible floods. Flowing through three Brazilian states, the Paraíba do Sul river is responsible for the supply and energy generation in several municipalities. Machine learning techniques have been studied with the aim of improving these predictions through the use of hydrological and hydrometeorological parameters. Furthermore, the predictive performance of the machine learning techniques are directly related to the quality of the training base and, moreover, to the set of hyperparameters used. The present study explores the combination of the Gradient Boosting technique coupled with a Genetic Algorithm to found the best set of hyperparameter to maximize the predicting performance of the Paraíba do Sul river streamflow.

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